Goal: This paper presents a fast and accurate patient-specific electrocardiogram (ECG)
classification and monitoring system. Methods: An adaptive implementation of 1-D�…

This paper proposes deep learning methods with signal alignment that facilitate the end-to-
end classification of raw electrocardiogram (ECG) signals into heartbeat types, ie, normal�…

Objective: Despitethe proliferation of numerous deep learning methods proposed for generic
ECG classification and arrhythmia detection, compact systems with the real-time ability and�…

We propose a fast and accurate patient-specific electrocardiogram (ECG) classification and
monitoring system using an adaptive implementation of 1D Convolutional Neural Networks�…

In this paper, we propose a transfer learning approach for Arrhythmia Detection and
Classification in Cross ECG Databases. This approach relies on a deep convolutional�…

This paper develops an end-to-end ECG signal classification algorithm based on a novel
segmentation strategy and 1D Convolutional Neural Networks (CNN) to aid the classification�…

This paper presents a novel deep learning framework for the inter-patient electrocardiogram
(ECG) heartbeat classification. A symbolization approach especially designed for ECG is�…

In this paper, we propose a semi-supervised learning-based ECG classification system for
detection of supraventricular ectopic beats (SVEB or S beats) and ventricular ectopic beats�…

A high performance electrocardiogram (ECG)-based arrhythmic beats classification system
is presented in this paper. The classifier was designed based on convolutional neural�…

The electrocardiogram (ECG) is a standard test used to monitor the activity of the heart.
Many cardiac abnormalities will be manifested in the ECG including arrhythmia which is a�…